Random
The Random Node.
The Random Node generates a random outcome, usually a number.
This Node can be set to three different
Modes (Advanced, Expert, and Standard). Each of these Modes offers a different set of Attributes that are explained below.
The Random Node Attributes.
This
Mode allows the user to choose whether the random generator is deterministic or not, and for the deterministic case, the seed to use.Attribute | Type | Description |
|---|---|---|
Is Deterministic | Bool | Whether the random generator is deterministic or not. |
Seed | Int (only available when Is Deterministic is set to true) | The Seed to use for the deterministic random generator. |
This Mode has a Drop-down menu from which the probability distribution used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.
Attribute | Type | Description |
|---|---|---|
Distribution | Drop-down | The probability distribution that the random generator will use. |
Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.
Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.
Attribute | Type | Description |
|---|---|---|
Probability of 'true' | Float (between 0 and 1) | The probability that the outcome will be true. |
Probability distribution of the number of successes in a sequence of independent experiments, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Whether the outcome will be an Int or Byte. |
Probability of 'true' | Float | The probability that the outcome of each trial is true. |
Number of trials | Int | The number of independent experiments, each one with probability of success Probability of 'true'. |
Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.
Attribute | Type | Description |
|---|---|---|
Mean | Float | The mean value of the distribution. |
Standard deviation | Float | The standard deviation of the distribution. |
Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Wheter the outcome will be an Int or Byte. |
Mean | Float | The mean value of the distribution. |
- Uniform
Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Whether an Int, Float, or Byte will be generated. |
Minimum | Defined in the Data Type Attribute | The lower bound of the interval from which the random number will be extracted. |
Maximum | Defined in the Data Type Attribute | The upper bound of the interval from which the random number will be extracted. |
This
Mode allows to choose from a list of several types of random generators.Attribute | Type | Description |
|---|---|---|
Generator | Drop-down | The type of random generator to use. |
Seed | Int (not available for non_deterministic Generator) | The Seed to use for the random generator. |
This
Mode has a Drop-down menu from which the probability distribution to be used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.Attribute | Type | Description |
|---|---|---|
Distribution | Drop-down | The probability distribution that the random generator will use. |
Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.
Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.
Attribute | Type | Description |
|---|---|---|
Probability of 'true' | Float (between 0 and 1) | The probability that the outcome will be true. |
Probability distribution of the number of successes in a sequence of independent experiment, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Whether the outcome will be an Int or Byte. |
Probability of 'true' | Float | The probability that the outcome of each trial is true. |
Number of trials | Int | The number of independent experiments performed, each one with probability of success Probability of 'true'. |
Probability distribution that resembles a normal distribution but with a taller peak, whose tails decay slower. Its parameters are the location of the peak and the scale - the latter defines its width.
Attribute | Type | Description |
|---|---|---|
Location | Float | Defines where the peak is. |
Scale | Float | Half the width of the probability density function at half the maximum height. |
Probability distribution of a sum of the squares of a number of independent normal random variables. The number of normal random variables is called the degrees of freedom of the Chi-squared distribution.
Attribute | Type | Description |
|---|---|---|
Degrees of freedom | Float | Number of independent normal random variables that are summed. |
Probability distribution of the time between events in a Poisson process. Its parameter is the rate at which the events in the Poisson process occur.
Attribute | Type | Description |
|---|---|---|
Rate | Float | Rate at which the events in the Poisson process occur. |
Limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables.
Attribute | Type | Description |
|---|---|---|
Location | Float | Defines where the peak is. |
Scale | Float | Defines how spread out the values are. |
Ratio of two independent random variables with chi-squared distributions, each one divided by its corresponding number of degrees of freedom for scaling.
Attribute | Type | Description |
|---|---|---|
Denominator Dof | Float | Degrees of freedom of the chi-squared random variable in the denominator. |
Numerator DoF | Float | Degrees of freedom of the chi-squared random variable in the numerator. |
Maximum entropy probability distribution for a random variable, whose mean is the product between the shape and scale, which are the two parameters of the Gamma distribution.
Attribute | Type | Description |
|---|---|---|
Shape | Float | Modifies the shape of the probability distribution. |
Scale | Float | Defines how spread out are the values. |
The probability distribution of the number of experiments with a Bernoulli distribution needed to get one success.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Whether the output is an Int or Byte. |
Probability of 'true' | Float (between 0 and 1) | The probability of success in the Bernoulli trials. |
Probability distribution of a random variable whose logarithm has a normal distribution.
Attribute | Type | Description |
|---|---|---|
Mean | Float | The mean value of the logarithm of the distribution. |
Standard deviation | Float | The standard deviation of the logarithm of the distribution. |
Probability distribution of the number of successes in a sequence of independent experiments, each with two possible outcomes: success and failure, before a specified non-random number of failures occur. The parameters for this probability distribution are the probability of a successful outcome in each experiment and the number of failures until the experiments stop.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Whether the outcome is an Int or Byte. |
Probability of 'true' | Float (between 0 and 1) | The probability that the outcome of each trial is true. |
Number of trials | Int | The number of failures to occur until the experiments stop. |
Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.
Attribute | Type | Description |
|---|---|---|
Mean | Float | The mean value of the distribution. |
Standard deviation | Float | The standard deviation of the distribution. |
Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Wheter the outcome will be an Int or Byte. |
Mean | Float | The mean value of the distribution. |
Probability distribution that arises when estimating the mean of a normally-distributed statistical population with a small sample size and unknown standard deviation. Its parameter is the number of degrees of freedom, which is the number of observations taken from a normal distribution minus one.
Attribute | Type | Description |
|---|---|---|
Degrees of freedom | Float | The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution. |
- Uniform
Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Whether an Int, Float, or Byte will be generated. |
Minimum | Defined in the Data Type Attribute | The lower bound of the interval from which the random number will be extracted. |
Maximum | Defined in the Data Type Attribute | The upper bound of the interval from which the random number will be extracted. |
Attribute | Type | Description |
|---|---|---|
Shape | Float | Defines the shape of the probability distribution. |
Scale | Float | Defines how spread out the values of the probability distribution are. |
Attribute | Type | Description |
|---|---|---|
Data Type | Drop-down | Whether an Int, Float, or Byte will be generated. |
Minimum | Defined in the Data Type Attribute | The lower bound of the interval from which the random number will be extracted. |
Maximum | Defined in the Data Type Attribute | The upper bound of the interval from which the random number will be extracted. |
Input | Type | Description |
|---|---|---|
Pulse Input (►) | Pulse | A standard Input Pulse, to trigger the execution of the Node. |
Output | Type | Description |
|---|---|---|
Pulse Output (►) | Pulse | A standard Output Pulse, to move onto the next Node along the Logic Branch, once this Node has finished its execution. |
Output | Depends on the Mode and Distribution | The random outcome that was generated. |
Last modified 3mo ago